Modified K-Nearest Neighbor Fuzzy Classifier Using Group Prototypes and Its Application to Skin Segmentation

Abstract

This paper describes proposed modifications to the K-NN classifier, i.e., Modified Fuzzy KNN (MFKNN) to address some complexity drawbacks of KNN. MFKNN calculates group prototypes from several patterns belonging to the same class and uses these prototypes for the recognition of patterns. Number of prototypes created by MFKNN classifier is dependent on the distance factor d. More prototypes are created for smaller value of d and vice versa. Also a fuzzy logic layer is added to it to increase the prediction accuracy of the classifier. We have compared performance of original KNN and MFKNN using skin segmentation dataset. From the experimentation, one can conclude that performance of MFKNN is better than original KNN, in terms of percentage recognition rate and recall time per pattern, classification and classification time. MFKNN thus has increased the scope of original KNN for its application to large data sets, which was not possible previously.